 2.1.1E: What are some benefits of representing data sets using frequency di...
 2.1.3E: What is the difference between class limits and class boundaries?
 2.1.2E: Why should the number of classes in a frequency distribution be bet...
 2.1.4E: What is the difference between relative frequency and cumulative fr...
 2.1.5E: After constructing an expanded frequency distribution, what should ...
 2.1.6E: What is the difference between a frequency polygon and an ogive?
 2.1.7E: True or False? In Exercise, determine whether the statement is true...
 2.1.8E: True or False? In Exercise, determine whether the statement is true...
 2.1.9E: True or False? In Exercise, determine whether the statement is true...
 2.1.10E: True or False? In Exercise, determine whether the statement is true...
 2.1.11E: In Exercise, use the minimum and maximum data entries and the numbe...
 2.1.12E: In Exercise, use the minimum and maximum data entries and the numbe...
 2.1.13E: In Exercise, use the minimum and maximum data entries and the numbe...
 2.1.14E: In Exercise, use the minimum and maximum data entries and the numbe...
 2.1.15E: Reading a Frequency Distribution In Exercise, use the frequency dis...
 2.1.16E: Reading a Frequency Distribution In Exercise 16, use the frequency ...
 2.1.17E: Use the frequency distribution In Exercise, to construct an expande...
 2.1.18E: Use the frequency distribution In Exercise, to construct an expande...
 2.1.19E: Graphical Analysis In Exercise, use the frequency histogram to(a) d...
 2.1.20E: Graphical Analysis In Exercise, use the frequency histogram to(a) d...
 2.1.22E: Graphical Analysis In Exercise, use the ogive to approximate(a) the...
 2.1.24E: Use the ogive In Exercise, to approximate(a) the cumulative frequen...
 2.1.26E: Graphical Analysis In Exercise, use the relative frequency histogra...
 2.1.27E: Graphical Analysis In Exercise, use the frequency polygon to identi...
 2.1.28E: Graphical Analysis In Exercise, use the frequency polygon to identi...
 2.1.29E: Constructing a Frequency Distribution In Exercise, construct a freq...
 2.1.30E: Constructing a Frequency Distribution In Exercise, construct a freq...
 2.1.31E: Constructing a Frequency Distribution and a Frequency Histogram In ...
 2.1.32E: Constructing a Frequency Distribution and a Frequency Histogram In ...
 2.1.33E: Constructing a Frequency Distribution and a Frequency Histogram In ...
 2.1.38E: Constructing a Frequency Distribution and a Relative Frequency Hist...
 2.1.37E: Constructing a Frequency Distribution and a Relative Frequency Hist...
 2.1.39E: Constructing a Cumulative Frequency Distribution and an Ogive In Ex...
 2.1.40E: Constructing a Cumulative Frequency Distribution and an Ogive In Ex...
 2.1.42E: Constructing a Frequency Distribution and a Frequency Polygon In Ex...
 2.1.43E: In Exercise, use the data set and the indicated number of classes t...
 2.1.44E: In Exercise, use the data set and the indicated number of classes t...
 2.1.46E: Writing Use the data set listed and technology to create frequency ...
 2.1.47E: What Would You Do? You work at a bank and are asked to recommend th...
 2.1.48E: What Would You Do? You work in the admissions department for a coll...
Solutions for Chapter 2.1: Elementary Statistics: Picturing the World 5th Edition
Full solutions for Elementary Statistics: Picturing the World  5th Edition
ISBN: 9780321693624
Solutions for Chapter 2.1
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Arithmetic mean
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average

Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.

Bivariate normal distribution
The joint distribution of two normal random variables

Categorical data
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

Confounding
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.

Correlation matrix
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the offdiagonal elements rij are the correlations between Xi and Xj .

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Curvilinear regression
An expression sometimes used for nonlinear regression models or polynomial regression models.

Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Discrete distribution
A probability distribution for a discrete random variable

Empirical model
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.

Event
A subset of a sample space.

Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.

F distribution.
The distribution of the random variable deined as the ratio of two independent chisquare random variables, each divided by its number of degrees of freedom.

False alarm
A signal from a control chart when no assignable causes are present

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

Gamma function
A function used in the probability density function of a gamma random variable that can be considered to extend factorials

Geometric mean.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .

Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.

Hat matrix.
In multiple regression, the matrix H XXX X = ( ) ? ? 1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .